commit a3ea09d3f8d7736fe5ae9eb8f8ce92487562bc21 Author: ModelHub XC Date: Sun May 31 18:07:14 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: OpenDataArena/Qwen2.5-7B-ODA-Mixture-100k Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..8a7ddcb --- /dev/null +++ b/.gitattributes @@ -0,0 +1,64 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text + + +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zstandard filter=lfs diff=lfs merge=lfs -text +*.tfevents* filter=lfs diff=lfs merge=lfs -text +*.db* filter=lfs diff=lfs merge=lfs -text +*.ark* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*data* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.meta filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.index filter=lfs diff=lfs merge=lfs -text + +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.gguf* filter=lfs diff=lfs merge=lfs -text +*.ggml filter=lfs diff=lfs merge=lfs -text +*.llamafile* filter=lfs diff=lfs merge=lfs -text +*.pt2 filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text + +checkpoint-3708/tokenizer.json filter=lfs diff=lfs merge=lfs -text +model-00001-of-00004.safetensors filter=lfs diff=lfs merge=lfs -text +checkpoint-3708/merges.txt filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text +vocab.json filter=lfs diff=lfs merge=lfs -text +merges.txt filter=lfs diff=lfs merge=lfs -text +checkpoint-3708/model-00004-of-00004.safetensors filter=lfs diff=lfs merge=lfs -text +checkpoint-3708/model-00003-of-00004.safetensors filter=lfs diff=lfs merge=lfs -text +training_args.bin filter=lfs diff=lfs merge=lfs -text +checkpoint-3708/model-00001-of-00004.safetensors filter=lfs diff=lfs merge=lfs -text +model-00002-of-00004.safetensors filter=lfs diff=lfs merge=lfs -text +checkpoint-3708/model-00002-of-00004.safetensors filter=lfs diff=lfs merge=lfs -text +model-00003-of-00004.safetensors filter=lfs diff=lfs merge=lfs -text +checkpoint-3708/training_args.bin filter=lfs diff=lfs merge=lfs -text +checkpoint-3708/vocab.json filter=lfs diff=lfs merge=lfs -text +model-00004-of-00004.safetensors filter=lfs diff=lfs merge=lfs -text \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..bfb597a --- /dev/null +++ b/README.md @@ -0,0 +1,244 @@ +--- +base_model: Qwen/Qwen2.5-7B-Base +library_name: transformers +pipeline_tag: text-generation +datasets: +- OpenDataArena/ODA-Mixture-100k +tags: +- qwen2.5 +- sft +- opendataarena +- oda-mixture-100k +license: apache-2.0 +language: +- en +metrics: +- accuracy +--- + +# Qwen2.5-7B-ODA-Mixture-100k +Leaderboard Performance + +Qwen2.5-7B-ODA-Mixture-100k is a supervised fine-tuned (SFT) model built on top of **Qwen2.5-7B-Base**, trained with **[ODA-Mixture-100k](https://huggingface.co/datasets/OpenDataArena/ODA-Mixture-100k)**. This training set is curated by mixing top-performing open corpora selected via the *[OpenDataArena](https://opendataarena.github.io)* leaderboard, and refined through deduplication and benchmark decontamination, aiming to improve the model’s general capabilities across **General**, **Math**, **Code**, and **Reasoning** domains under a compact ~100K data budget. + +--- + +## 🧠 Model Summary + +- **Base Model**: `Qwen/Qwen2.5-7B-Base` +- **Training Data**: `OpenDataArena/ODA-Mixture-100k` +- **Domain Coverage**: General, Math, Code, Reasoning +- **Scale (selected training set)**: ~**100K** samples +- **Goal**: Achieve significant general-purpose gains with a compact curated dataset, improving multi-domain reasoning and problem-solving ability. + +--- + +## ⚙️ Training Data Curation Pipeline + +ODA-Mixture-100k is built by following a single rule: **trust the OpenDataArena leaderboard**. + +### 1️⃣ Data Collection + +We chose **LIMO** as our foundation because it achieves a high ranking on the ODA overall leaderboard with very few samples. This efficiency allows us to establish a strong reasoning baseline. We then augment this core with **AM-Thinking-v1-Distilled-math** and **AM-Thinking-v1-Distilled-code**, the top-performing and efficient datasets on the ODA Math and Code leaderboards, to enhance specialized domain capabilities. + +### 2️⃣ Deduplication & Decontamination + +We first perform **exact deduplication** over all questions to remove identical items, and then run **benchmark decontamination** to reduce evaluation leakage by removing overlaps with standard and competition benchmarks. + +### 3️⃣ Data Selection + +To adhere to our ~100K data budget while maximizing the impact of each sample, we employ semantic clustering to map the overall data distribution. Within each cluster, we preferentially sample the most challenging instances, using sequence length as a practical proxy for reasoning complexity and problem difficulty. + +--- + +## 📚 Training Data Source Composition + +| Source | Count | Percentage | +|---|---:|---:| +| LIMO | 817 | 0.81% | +| AM-Thinking-Distilled-math | 50,244 | 49.59% | +| AM-Thinking-Distilled-code| 50,245 | 49.60% | + +--- + +## 🧩 Data Format + +The training data sample format is as follows (aligned with the dataset schema): + +```json +{ + "id": "unique_identifier", + "source": "data source", + "question": "textual question or instruction", + "response": "textual response" +} +``` + +--- + +## 📈 Performance + +Qwen2.5-7B-ODA-Mixture-100k is evaluated as an SFT model built on **Qwen2.5-7B-Base** across the full ODA benchmark suite spanning four domains: +- **General (DROP, IFEVAL, AGIEVAL, MMLU-Pro)** +- **Math (GSM8K, MATH500, Omni-Math, OlympiadBench, AIME2024)** +- **Code (HumanEval, MBPP, LCB (V5), HumanEval+)** +- **Reasoning (ARC-C, BBH, CALM, KOR-BENCH)**. + +We observe consistent improvements over the base checkpoint, with particularly strong gains on several benchmarks. + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ Leaderboard Performance Comparison. Best scores in bold, second-best underlined. Eff. denotes Data Efficiency. +
Model / Training DataSizeEff.GeneralMathCodeReasoningAVG
Qwen2.5-7B-Base
Qwen2.5-7B-Base--51.439.850.142.746.0
OpenThoughts3-1.2M1.2M+0.01145.571.867.054.359.6
OmniThought-0528365k+0.02747.171.247.657.255.8
SYNTHETIC-2-SFT-verified105k+0.08651.369.840.158.955.0
AM-Thinking-v1-Distilled-math558k+0.01657.777.439.544.854.8
LIMO817+9.92060.744.057.953.854.1
MiroMind-M1-SFT-719K719k+0.00652.071.026.351.550.2
AM-Thinking-v1-Distilled-code324k+0.02449.952.368.744.453.8
Light-R1-SFTData79k+0.08455.564.438.851.952.7
ODA-Mixture-500k500k+0.03963.472.866.759.665.6
ODA-Mixture-100k100k+0.14956.871.264.451.561.0
+
+ +--- + +## 🌐 About OpenDataArena + +[OpenDataArena](https://opendataarena.github.io/) is an open research platform dedicated to **discovering, evaluating, and advancing high-quality datasets for AI post-training**. It provides a transparent, data-centric ecosystem to support reproducible dataset evaluation and sharing. + +**Key Features:** +- 🏆 **Dataset Leaderboard** — helps researchers identify **the most valuable and high-quality datasets across different domains** +- 📊 **Detailed Evaluation Scores** — provides **comprehensive metrics** to assess data quality, complexity, difficulty, etc. +- 🧰 **Data Processing Toolkit** — [OpenDataArena-Tool](https://github.com/OpenDataArena/OpenDataArena-Tool) offers an open-source pipeline for dataset curation and scoring. + +--- + +## 🚀 Usage + +Model repo: `OpenDataArena/Qwen2.5-7B-ODA-Mixture-100k`. Below is a minimal runnable example for loading and inference: + +```python +from transformers import AutoModelForCausalLM, AutoTokenizer + +MODEL_ID = "OpenDataArena/Qwen2.5-7B-ODA-Mixture-100k" + +tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True) +model = AutoModelForCausalLM.from_pretrained(MODEL_ID, device_map="auto", trust_remote_code=True) + +messages = [ + {"role": "user", "content": "Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?"}, +] +text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) +inputs = tokenizer([text], return_tensors="pt").to(model.device) + +outputs = model.generate( + **inputs, + max_new_tokens=256, + do_sample=True, + temperature=0.7, + top_p=0.9, +) +print(tokenizer.decode(outputs[0], skip_special_tokens=True)) +``` + +--- + +## 📚 Citation + +If you use this model or its training data (ODA-Mixture-100k), please cite: + +```bibtex +@article{gao2025closing, + title={Closing the Data Loop: Using OpenDataArena to Engineer Superior Training Datasets}, + author={Gao, Xin and Wang, Xiaoyang and Zhu, Yun and Cai, Mengzhang and He, Conghui and Wu, Lijun}, + journal={arXiv preprint arXiv:2601.09733}, + year={2025} +} +``` +```bibtex +@article{cai2025opendataarena, + title={OpenDataArena: A Fair and Open Arena for Benchmarking Post-Training Dataset Value}, + author={Cai, Mengzhang and Gao, Xin and Li, Yu and Lin, Honglin and Liu, Zheng and Pan, Zhuoshi and Pei, Qizhi and Shang, Xiaoran and Sun, Mengyuan and Tang, Zinan and others}, + journal={arXiv preprint arXiv:2512.14051}, + year={2025} +} +``` + diff --git a/README.md.backup b/README.md.backup new file mode 100644 index 0000000..b9f9606 --- /dev/null +++ b/README.md.backup @@ -0,0 +1,61 @@ +--- +library_name: transformers +license: other +base_model: /mnt/dhwfile/raise/user/caimengzhang/huggingface/hub/Qwen2.5-7B +tags: +- llama-factory +- full +- generated_from_trainer +model-index: +- name: seed-42 + results: [] +--- + + + +# seed-42 + +This model is a fine-tuned version of [/mnt/dhwfile/raise/user/caimengzhang/huggingface/hub/Qwen2.5-7B](https://huggingface.co//mnt/dhwfile/raise/user/caimengzhang/huggingface/hub/Qwen2.5-7B) on the overall-v6b-100k dataset. + +## Model description + +More information needed + +## Intended uses & limitations + +More information needed + +## Training and evaluation data + +More information needed + +## Training procedure + +### Training hyperparameters + +The following hyperparameters were used during training: +- learning_rate: 5e-05 +- train_batch_size: 2 +- eval_batch_size: 8 +- seed: 42 +- distributed_type: multi-GPU +- num_devices: 8 +- gradient_accumulation_steps: 2 +- total_train_batch_size: 32 +- total_eval_batch_size: 64 +- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments +- lr_scheduler_type: cosine +- lr_scheduler_warmup_ratio: 0.1 +- num_epochs: 3.0 + +### Training results + + + +### Framework versions + +- Transformers 4.55.0 +- Pytorch 2.6.0+cu124 +- Datasets 3.2.0 +- Tokenizers 0.21.0 diff --git a/added_tokens.json b/added_tokens.json new file mode 100644 index 0000000..482ced4 --- /dev/null +++ b/added_tokens.json @@ -0,0 +1,24 @@ +{ + "": 151658, + "": 151657, + "<|box_end|>": 151649, + "<|box_start|>": 151648, + "<|endoftext|>": 151643, + "<|file_sep|>": 151664, + "<|fim_middle|>": 151660, + "<|fim_pad|>": 151662, + "<|fim_prefix|>": 151659, + "<|fim_suffix|>": 151661, + "<|im_end|>": 151645, + "<|im_start|>": 151644, + "<|image_pad|>": 151655, + "<|object_ref_end|>": 151647, + "<|object_ref_start|>": 151646, + "<|quad_end|>": 151651, + "<|quad_start|>": 151650, + "<|repo_name|>": 151663, + "<|video_pad|>": 151656, + "<|vision_end|>": 151653, + "<|vision_pad|>": 151654, + "<|vision_start|>": 151652 +} diff --git a/all_results.json b/all_results.json new file mode 100644 index 0000000..e27df72 --- /dev/null +++ b/all_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 3.0, + "total_flos": 6.364819972765516e+18, + "train_loss": 0.2649726264235014, + "train_runtime": 208629.7363, + "train_samples_per_second": 0.569, + "train_steps_per_second": 0.018 +} \ No newline at end of file diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..d153f82 --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,4 @@ +{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% endif %}{% if system_message is defined %}{{ 'System: ' + system_message + '<|endoftext|>' + ' +' }}{% endif %}{% for message in loop_messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ 'Human: ' + content + '<|endoftext|>' + ' +Assistant:' }}{% elif message['role'] == 'assistant' %}{{ content + '<|endoftext|>' + ' +' }}{% endif %}{% endfor %} \ No newline at end of file diff --git a/checkpoint-3708/added_tokens.json b/checkpoint-3708/added_tokens.json new file mode 100644 index 0000000..482ced4 --- /dev/null +++ b/checkpoint-3708/added_tokens.json @@ -0,0 +1,24 @@ +{ + "": 151658, + "": 151657, + "<|box_end|>": 151649, + "<|box_start|>": 151648, + "<|endoftext|>": 151643, + "<|file_sep|>": 151664, + "<|fim_middle|>": 151660, + "<|fim_pad|>": 151662, + "<|fim_prefix|>": 151659, + "<|fim_suffix|>": 151661, + "<|im_end|>": 151645, + "<|im_start|>": 151644, + "<|image_pad|>": 151655, + "<|object_ref_end|>": 151647, + "<|object_ref_start|>": 151646, + "<|quad_end|>": 151651, + "<|quad_start|>": 151650, + "<|repo_name|>": 151663, + "<|video_pad|>": 151656, + "<|vision_end|>": 151653, + "<|vision_pad|>": 151654, + "<|vision_start|>": 151652 +} diff --git a/checkpoint-3708/chat_template.jinja b/checkpoint-3708/chat_template.jinja new file mode 100644 index 0000000..d153f82 --- /dev/null +++ b/checkpoint-3708/chat_template.jinja @@ -0,0 +1,4 @@ +{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% endif %}{% if system_message is defined %}{{ 'System: ' + system_message + '<|endoftext|>' + ' +' }}{% endif %}{% for message in loop_messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ 'Human: ' + content + '<|endoftext|>' + ' +Assistant:' }}{% elif message['role'] == 'assistant' %}{{ content + '<|endoftext|>' + ' +' }}{% endif %}{% endfor %} \ No newline at end of file diff --git a/checkpoint-3708/config.json b/checkpoint-3708/config.json new file mode 100644 index 0000000..fa2559a --- /dev/null +++ b/checkpoint-3708/config.json @@ -0,0 +1,59 @@ +{ + "architectures": [ + "Qwen2ForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 151643, + "eos_token_id": 151643, + "hidden_act": "silu", + "hidden_size": 3584, + "initializer_range": 0.02, + "intermediate_size": 18944, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + 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